| Scan Performance | |||
|---|---|---|---|
| sub | correct | conf | conf_correct |
| 01 | 0.925 | 0.025 | 0.025 |
| 02 | 0.850 | 0.750 | 0.725 |
| 03 | 0.650 | 0.225 | 0.175 |
| 04 | 0.950 | 0.950 | 0.925 |
| 05 | 0.175 | 0.300 | 0.125 |
| 06 | 0.775 | 0.725 | 0.675 |
| 07 | 0.750 | 0.000 | 0.000 |
| 08 | 0.475 | 0.725 | 0.450 |
| 09 | 0.975 | 0.575 | 0.575 |
| 10 | 0.875 | 0.625 | 0.625 |
| 11 | 0.900 | 0.800 | 0.775 |
| 12 | 0.800 | 0.900 | 0.750 |
| 13 | 0.900 | 0.925 | 0.850 |
| 14 | 0.375 | 0.700 | 0.350 |
## `summarise()` has grouped output by 'sub', 'nquestion', 'round_text'. You can override using the `.groups` argument.
## Joining, by = "sub"
## Joining, by = "sub"
| Prescan Performance | |||
|---|---|---|---|
| sub | correct | conf | conf_correct |
| 01 | 0.750 | 0.333 | 0.333 |
| 02 | 0.667 | 0.417 | 0.417 |
| 03 | 0.792 | 0.500 | 0.458 |
| 04 | 0.667 | 0.625 | 0.500 |
| 05 | 0.250 | 0.500 | 0.083 |
| 06 | 0.708 | 0.667 | 0.583 |
| 07 | 0.708 | 0.333 | 0.333 |
| 08 | 0.333 | 0.333 | 0.333 |
| 09 | 0.792 | 0.458 | 0.458 |
| 10 | 0.792 | 0.458 | 0.458 |
| 11 | 0.708 | 0.458 | 0.458 |
| 12 | 0.833 | 0.583 | 0.583 |
| 13 | 0.583 | 0.917 | 0.542 |
| 14 | 0.167 | 0.458 | 0.125 |
Excluding subject 05, 08, and 14. Should we exclude sub 08?
Notice: sub1/3/7 have very low confidence.
Participant were instructed to answer the expected destination for 3 times during the route: once at Same, once at Overlapping, and once at non-overlapping. They also indicated their confidence towards the choice (sure vs. unsure).
## `summarise()` has grouped output by 'round_text'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'nquestion', 'round'. You can override using the `.groups` argument.
ANVOA for Accuracy:
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 13 3.730942e+00 7.550778e-02 1.133747e-01
## 2 nquestion 1 13 4.380255e+01 1.667428e-05 * 6.132148e-01
## 3 round:nquestion 1 13 7.307024e-28 1.000000e+00 4.716745e-30
ANVOA for Confidence:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 13 7.770621 0.0153911293 * 0.121665133
## 2 nquestion 1 13 39.461197 0.0000282501 * 0.682622787
## 3 round:nquestion 1 13 1.368421 0.2630838178 0.006245121
ANVOA for high confidence accuracy:
## $ANOVA
## Effect DFn DFd F p p<.05 ges
## 1 round 1 13 16.64394 1.301752e-03 * 0.1575390
## 2 nquestion 1 13 42.51351 1.941367e-05 * 0.7109965
## 3 round:nquestion 1 13 2.60000 1.308648e-01 0.0199268
t-test for mean (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$m
## t = -1.5778, df = 13, p-value = 0.1386
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.25384859 0.03956288
## sample estimates:
## mean of the differences
## -0.1071429
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$m
## t = -0.88763, df = 13, p-value = 0.3909
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.2452767 0.1024196
## sample estimates:
## mean of the differences
## -0.07142857
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$m and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$m
## t = -3.1225, df = 13, p-value = 0.008089
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.18127197 -0.03301375
## sample estimates:
## mean of the differences
## -0.1071429
t-test for confidence (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$conf
## t = -0.80623, df = 13, p-value = 0.4346
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.13141455 0.05998598
## sample estimates:
## mean of the differences
## -0.03571429
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$conf
## t = -2.895, df = 13, p-value = 0.01253
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.46774245 -0.06797183
## sample estimates:
## mean of the differences
## -0.2678571
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$conf
## t = -1.5778, df = 13, p-value = 0.1386
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.25384859 0.03956288
## sample estimates:
## mean of the differences
## -0.1071429
t-test for high confidence accuracy (overlapping segment):
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 1) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 1) %>% .$cor_conf
## t = -0.52042, df = 13, p-value = 0.6115
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1839725 0.1125439
## sample estimates:
## mean of the differences
## -0.03571429
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 2) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 2) %>% .$cor_conf
## t = -3.3223, df = 13, p-value = 0.005506
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.44203426 -0.09368002
## sample estimates:
## mean of the differences
## -0.2678571
##
## Paired t-test
##
## data: sub_plot %>% filter(round == 1 & nquestion == 3) %>% .$cor_conf and sub_plot %>% filter(round == 2 & nquestion == 3) %>% .$cor_conf
## t = -2.8571, df = 13, p-value = 0.01347
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.28223856 -0.03919002
## sample estimates:
## mean of the differences
## -0.1607143
Accuracy per round:
## `summarise()` has grouped output by 'round'. You can override using the `.groups` argument.
Distribution of picture index:
Grouped in 10:
## `summarise()` has grouped output by 'npic_10', 'route'. You can override using the `.groups` argument.
Grouped in 5:
## `summarise()` has grouped output by 'npic_5', 'route'. You can override using the `.groups` argument.
Every picture:
## `summarise()` has grouped output by 'npic', 'route'. You can override using the `.groups` argument.
| Mean accuracy per subject | |
|---|---|
| sub | m |
| 01 | 0.9375 |
| 02 | 0.9375 |
| 03 | 1.0000 |
| 04 | 1.0000 |
| 06 | 1.0000 |
| 07 | 0.9375 |
| 09 | 1.0000 |
| 10 | 1.0000 |
| 11 | 1.0000 |
| 12 | 1.0000 |
| 13 | 0.8125 |
Average accuracy = 0.9659091
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.